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Waiting time at transit stops is found to be an influential policy variable for a passenger's decision on whether to undertake a given transit service. With regard to policy framework for improvement of operational service headway of a transit service and thereby its waiting time, the necessity to have knowledge on its critical value becomes inevitable. The critical value of waiting time for passengers waiting at transit stops is that duration beyond which passengers are found to be no more interested to wait for a that transit service. The paper demonstrates an approach for estimating the critical value of waiting time at urban transit stops with reference to public transport services such as city bus and shared-auto operational in Bhubaneswar, India. The critical value of waiting time is estimated from the point on cumulative distribution curve of waiting time frequency distribution, at which the maximum rate of change of the slope of curve occurs. The work assumes two positively skewed distributions such as gamma and log-normal for observed distributional pattern of waiting time. The work identifies that gamma distribution is comparatively fitting the observed data better than log-normal distribution. The study reveals that the critical value of waiting time for city bus passengers is about twice than that of shared auto passengers. Though, the study presents new information on critical values of waiting time with reference to an urban area of a developing country, it also demonstrates an experience on application of probability distribution functions for understanding distributional pattern of waiting time.

The article presents an investigation into selection of priority areas of intervention for improving travel condition of walk-accessed bus users within a bus stop catchment area. The work considers that propensity to use bus service by walk-accessed bus users is bi-directionally elastic to both the quality of walk-access facility, and the service characteristics of bus operation. In view of this, the current work attempts to consider two lists of attributes each from walk-accessed facility and from service characteristics of bus operation, and then users’ opinions on them are recorded in terms of perceived importance and satisfaction. The intervention areas are then identified by employing a two-dimensional analysis called Revised Importance Performance Analysis (R-IPA) using fuzzy-C means clustering. In this process, the factor structure and management scheme of the attributes are identified, and the list of intervention areas are finalized by comparing them. The attributes related to walk-access facility such as surface quality of sidewalk, interval between crosswalks, presence of dedicated sidewalk facility with adequate width, provision of adequate street lights and road surveillance are found to warrant immediate intervention. Similarly, the attributes related to service characteristics of bus operation such as frequency of bus service, in-vehicle journey time, and bus fare are also found to warrant immediate intervention.

The article presents a work on the assessment of urban bus stop infrastructure in Bhubaneswar, and identifies improvement needs for the same as per bus passengers’ requirement and satisfaction. The work proposes a rationally appealing assessment framework, which uses perceived information of the bus passengers on various infrastructural items collected in scales of their importance and satisfaction level. In wake of this assessment, a set of key-factors are first identified using Exploratory Factor Analysis (EFA) on the stated importance ratings on an array of items, and then those key-factors are ordered by using Relative to an Identified Distribution Integral Transformation (RIDIT) analysis on stated satisfaction rating of the same survey items. The assessment framework takes an attempt to exploit the strength of these two types of dataset, while preparing the policy framework for improvement. The ordering of key-factors is carried out for two types of bus commuter such as captive users and choice users. The work documents new evidences in terms of improvement requirement of bus stop infrastructure in any urban area of emerging economies like India.

Presently, there is no consensus on the factors which might influence a metro-commuter towards choosing the bicycling as a last mile connectivity option in Indian context. Hence, a broader understanding of the end-user preferences towards key set of determinants related to bicycle-metro sharing is necessary to formulate effective policy measures, which would subsequently lead to sustainable cities and improved well-being of commuters. This article is an attempt to demonstrate a scientific approach to prioritize among an important set of determinants influencing commuter’s decision to use bicycle as an access or egress mode to metro rail. The demonstrated methodology also makes an effort to understand the sources of heterogeneity towards user perception as well. Hyderabad, an Indian metropolitan city, which has recently introduced metro-rail, has been selected as the case study city. Initially, a set of key determinants related to bicycling in general and bicycle-metro integration in particular is identified and commuter’s perception towards these determinants is collected based on a typical five-point likert scale item. Subsequently, the sources of heterogeneity among user responses were explored by using Kruskal Wallis H test, a non-parametric statistical measure. Results clearly indicate that user perception towards several determinants were significantly influenced by trip-specific characteristics such as trip frequency, trip purpose etc. Overall, a clear evidence of heterogeneity among user perceptions on the factors indicates the need for conducting separate analyses for different socio-economic sub-groups. Prioritization of the identified determinants was carried out by an established MADM technique, namely, RIDIT-analysis for different categories of commuters.

The work documents an experience in evaluation of the in-service infrastructural facilities of walk-access feeder paths to urban local bus stops in the city of Bhubaneswar, India. The work follows a rational approach, whereby both the in-service condition and relative importance of the walk-access infrastructures are considered, by taking into account the perception of those bus commuters, who usually undertake walk-mode to access bus stops. This approach allows determination of the relative priority among improvement areas of the existing walk-access infrastructural facilities, by focusing on those aspects where the perceived in-service condition is poor, but the importance is high. In view of this, a set of walk-access infrastructural items are identified existing in the study area, and bus commuters’ perception on their in-service condition and their importance is recorded. Thereafter, a two-dimensional fuzzy C-means algorithm is employed in order to identify the improvement areas. The work identifies a set of 5 major improvement areas of walk-access infrastructure, such as presence of dedicated sidewalk facility, width of sidewalk, presence of crosswalks, quality of road surface, and presence of street lights on access path. Overall, the work documents valuable insights in the context of infrastructural facility improvements in walk-access feeder paths with regard to a mid-sized urban area.

The paper presents behavioral model of a primary location choice for home-based non-mandatory tours. The choice model addresses an array of non-mandatory tour purposes. Though some of them are found in literature, such as shopping, recreation and entertainment, many of them such as personal business, social, eat and escort are not explicitly addressed in literature. In this study, the primary location choice model is developed under a certain nesting hierarchy of a joint tour location and mode choice, where tour mode choice is generally found a foregone conclusion upon which location choice is conditioned. The work reveals a list of attractors influencing the primary location choice behavior of an individual for a specific non-mandatory tour purpose. The study highlights the need for more introduction of transit-oriented service across the state of California, which is found a preferred option by individuals for non-mandatory tours such as shopping, recreation or entertainment, social etc. The study is demonstrated using household travel diary data for California, which is revealed preference in nature.

In this paper, we create EEG data derived signatures for differentiating epileptic patients from normal individuals. Epilepsy is a neurological condition of human beings, mostly treated based on a patient’s seizure symptoms. Clinicians face immense difficulty in detecting epileptic patients. Here we define brain region-connection based signatures from EEG data with help of various machine learning techniques. These signatures will help the clinicians in detecting epileptic patients in general. Moreover, we define separate signatures by taking into account a few demographic features like gender and age. Such signatures may aid the clinicians along with the generalized epileptic signature in case of complex decisions.